Role of the C-terminal tail in regulating Proteinase Activated Receptor 2 (PAR2) signalling
Bibliographic record
Abstract
ABSTRACT The C-terminal tail of G-protein-coupled receptors contain important regulatory sites that enable interaction with intracellular signalling effectors. Here we examine the relative contribution of the C-tail serine/threonine phosphorylation sites (Ser 383-385 , Ser 387 -Thr 392 ) and the helix-8 palmitoylation site (Cys 361 ) in signalling regulation downstream of the proteolytically-activated GPCR, PAR2. We examined Gα q/11 -coupled calcium signalling, β-arrestin-1/-2 recruitment, and MAPK activation (p44/42 phosphorylation) by wild-type and mutant receptors expressed in a CRISPR/Cas9 PAR2-knockout HEK-293 cell background. We find that alanine substitution of the membrane proximal serine residues (Ser 383-385 Ala) had no effect on SLIGRL-NH 2 - or trypsin-stimulated β-arrestin recruitment. Alanine substitutions in the Ser 387 -Thr 392 cluster resulted in a large (∼50%) decrease in β-arrestin-1/2 recruitment triggered by the activating peptide, SLIGRL-NH 2, but was without effect on trypsin-activated β-arrestin-1/-2 recruitment. Additionally, we find that alanine substitution of the helix-8 cysteine residue (Cys 361 Ala) led to a (∼50%) decrease in β-arrestin-1/-2 recruitment in response to both trypsin and SLIGRL-NH 2 . We further show that Gα q/11 inhibition with YM254890, inhibited ERK phosphorylation by PAR2 agonists, while genetic deletion of β-arrestin-1/-2 by CRISPR/Cas9 enhanced MAPK activation. Knockout of β-arrestins also enhanced Gα q/11 -mediated calcium signalling. In line with these findings, C-tail serine/threonine and cysteine residue mutants that have decreased β-arrestin recruitment also showed enhanced ERK activation. Thus, our studies point to multiple mechanisms that regulate β-arrestin interaction with PAR2 to regulate receptor-mediated signalling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".